Clinical Data Quality Analysis System Using Modular Segmentation
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Solution Overview
Problem
Existing medical clinical data quality management systems fail to perform multi-link classified acquisition and analysis of medical behaviors, leading to inadequate reflection of clinical data quality at various stages of management.
Innovation Solution
A medical clinical data quality analysis system based on big data, which includes modules for acquiring, analyzing, and processing authenticity, compliance, process, and conclusive data across admission, hospitalization, and discharge links, using factors such as authenticity and compliance factors to generate quality coefficients and signals for quality inspection and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single-disease model is used for clinical quality monitoring, then the system complexity is reduced, but the ability to perform multi-link classified acquisition of medical behaviors is lost
Solution Approach 1:
The patent divides the clinical quality monitoring system into multiple independent modules: data acquisition module, data cleaning module, quality evaluation module, and feedback module. Each module handles specific tasks in the medical behavior workflow, enabling multi-link classified acquisition while maintaining manageable system complexity through modular architecture.
2Measurement precision
If comprehensive data acquisition is implemented across all medical behavior links, then the quality reflection capability is improved, but the data processing complexity increases
Solution Approach 1:
The patent introduces a data cleaning module as an intermediary between data acquisition and quality evaluation. This module standardizes and processes raw medical behavior data, transforming unstructured multi-link data into structured formats suitable for quality assessment, thereby reducing processing complexity while maintaining comprehensive data collection.
Solution Approach 2:
The system transforms raw medical behavior data into standardized quality indicators through parameter changes. By converting diverse data types (text, numbers, timestamps) into unified quality metrics, the system achieves comprehensive quality reflection while simplifying subsequent analysis through parameter standardization.
3Speed
If real-time quality assessment is performed at all management stages, then the timeliness of quality control is improved, but the computational resource consumption increases
Solution Approach 1:
The patent performs preliminary data cleaning and standardization before quality evaluation. By pre-processing data into structured formats with standardized parameters, the system reduces computational burden during real-time quality assessment, enabling timely control without excessive resource consumption.
Solution Approach 2:
The system implements quality assessment at key decision points in the medical workflow rather than continuously at every stage. This partial action approach maintains timely quality control at critical moments while avoiding unnecessary computational overhead from constant monitoring at all possible stages.
Data Source
AI summary
The present invention discloses a medical clinical data quality analysis system based on big data, which relates to the field of medical big data technologies. The present invention is used to solve the technical problems that multi-link classified acquisition cannot be carried out on medical behaviors, and the quality of clinical data cannot be analyzed and reflected at the front end, middle link, and tail end of management. In the present invention, authenticity data, compliance data, process data and conclusive data are comprehensively acquired in admission, hospitalization, and discharge links of medical clinical data quality management, which is conducive to implementing multi-link classified acquisition of clinical medical behaviors, thereby facilitating subsequent dynamic and orderly quality analysis and control of clinical data.